Overview

In the past few years, FinTech investments have drastically increased across industries, and banks, in particular, are taking interest in this cutting-edge technology through digitization of their activities even in understanding the customer needs. An informed insight of the customer needs leads to proper decision making which will lead to the revenue increase. The success rate of the banking marketing strategies and implementation of these decisions can be made accurate by comparing various Machine Learning techniques to analyze and make predictions based on the bank’s user engagement.

Goal

The purpose of building the ML models is to predict whether the client will subscribe for a term deposit and will focus on marking efforts on these clients. On the other hand, analyzing the marking campaign that the bank has made most recently and the identification of the patterns will help to draw conclusions in order to improve further marketing campaigns in the future. For the whole project work, have a look at my Github repository.

Data information

In this project, we are going to use use the already existing bank marketing dataset (“Bank-additional-full.csv) downloaded from the UCI Machine Learning dataset repository.

Data understanding

Understanding of the data helps to better the implementation of the best Machine learning algorithm for deployment to solve a data problem. To do this we can use the describe() function to get a summary of the data.

Checking for missing values

We are going to use the following class to check for missing values in our dataset.

#analysis #banking #machine-learning #data-visualization